import os, logging, datetime, json, random import gradio as gr import numpy as np import torch import re_matching import utils from infer import infer, latest_version, get_net_g import gradio as gr from config import config from tools.webui import reload_javascript, get_character_html logging.basicConfig( level=logging.INFO, format='[%(levelname)s|%(asctime)s]%(message)s', datefmt='%Y-%m-%d %H:%M:%S' ) device = config.webui_config.device if device == "mps": os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" hps = utils.get_hparams_from_file(config.webui_config.config_path) version = hps.version if hasattr(hps, "version") else latest_version net_g = get_net_g(model_path=config.webui_config.model, version=version, device=device, hps=hps) with open("./css/style.css", "r", encoding="utf-8") as f: customCSS = f.read() with open("./assets/lines.json", "r", encoding="utf-8") as f: full_lines = json.load(f) def speak_fn( text: str, exceed_flag, speaker="TalkFlower_CNzh", sdp_ratio=0.2, # SDP/DP混合比 noise_scale=0.6, # 感情 noise_scale_w=0.6, # 音素长度 length_scale=0.9, # 语速 language="ZH", reference_audio=None, emotion=4, interval_between_para=0.2, # 段间间隔 interval_between_sent=1, # 句间间隔 ): while text.find("\n\n") != -1: text = text.replace("\n\n", "\n") if len(text) > 100: logging.info(f"Too Long Text: {text}") if exceed_flag: text = "不要超过100字!" audio_value = "./assets/audios/nomorethan100.wav" else: text = "这句太长了,憋坏我啦!" audio_value = "./assets/audios/overlength.wav" exceed_flag = not exceed_flag else: audio_list = [] if len(text) > 42: logging.info(f"Long Text: {text}") para_list = re_matching.cut_para(text) for p in para_list: audio_list_sent = [] sent_list = re_matching.cut_sent(p) for s in sent_list: audio = infer( s, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, sid=speaker, language=language, hps=hps, net_g=net_g, device=device, reference_audio=reference_audio, emotion=emotion, ) audio_list_sent.append(audio) silence = np.zeros((int)(44100 * interval_between_sent)) audio_list_sent.append(silence) if (interval_between_para - interval_between_sent) > 0: silence = np.zeros((int)(44100 * (interval_between_para - interval_between_sent))) audio_list_sent.append(silence) audio16bit = gr.processing_utils.convert_to_16_bit_wav(np.concatenate(audio_list_sent)) # 对完整句子做音量归一 audio_list.append(audio16bit) else: logging.info(f"Short Text: {text}") silence = np.zeros(hps.data.sampling_rate // 2, dtype=np.int16) with torch.no_grad(): for piece in text.split("|"): audio = infer( piece, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, sid=speaker, language=language, hps=hps, net_g=net_g, device=device, reference_audio=reference_audio, emotion=emotion, ) audio16bit = gr.processing_utils.convert_to_16_bit_wav(audio) audio_list.append(audio16bit) audio_list.append(silence) # 将静音添加到列表中 audio_concat = np.concatenate(audio_list) audio_value = (hps.data.sampling_rate, audio_concat) return gr.update(value=audio_value, autoplay=True), get_character_html(text), exceed_flag, gr.update(interactive=True) def submit_lock_fn(): return gr.update(interactive=False) def init_fn(): gr.Info("2023-11-24: 优化长句生成效果;增加示例;更新了一些小彩蛋;画了一些大饼)") gr.Info("Only support Chinese now. Trying to train a mutilingual model. 欢迎在 Community 中提建议~") index = random.randint(1,7) welcome_text = get_sentence("Welcome", index) return get_character_html(welcome_text) #gr.update(value=f"./assets/audios/Welcome{index}.wav", autoplay=False), def get_sentence(category, index=-1): if index == -1: index = random.randint(1, len(full_lines[category])) return full_lines[category][f"{index}"]